量子概率編碼遺傳算法及其應(yīng)用
Quantum Probability Coding Genetic Algorithm and Its Applications
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摘要: 該文提出了一種基于染色體量子概率編碼的遺傳算法--QCGA。與傳統(tǒng)遺傳算法不同,在QCGA中, 單個(gè)個(gè)體不再表示某一個(gè)確定解,而是解的取值概率分布,覆蓋整個(gè)解空間;各個(gè)個(gè)體獨(dú)立并行演化,個(gè)體間通過一個(gè)新的交叉算子實(shí)現(xiàn)演化信息的交換,同時(shí)設(shè)計(jì)了一個(gè)新的變異算子以增強(qiáng)算法的局部尋優(yōu)能力。為了充分考察該算法的有效性和先進(jìn)性,將其應(yīng)用于典型函數(shù)優(yōu)化、0-1背包問題和時(shí)間序列中頻繁結(jié)構(gòu)模式搜索等問題的求解。實(shí)驗(yàn)結(jié)果表明,與現(xiàn)有同類算法相比,該算法在具有很高搜索效率的同時(shí),仍能維持很高的種群多樣性, 因而適用于復(fù)雜優(yōu)化問題的求解。
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關(guān)鍵詞:
- 遺傳算法; 量子概率編碼; 交叉算子; 變異算子
Abstract: A Quantum probability Coding Genetic Algorithm-QCGA is proposed, which is different from classical GAs. In QCGA, single individual represents a probability distribution of solutions, which covers the whole solution space. Individuals in QCGA evolve independently and in parallel. A new crossover operator is designed to implement the information exchange among individuals. A new mutation operator is also design to prevent the algorithm from falling into local optima. To study the efficiency and advantage of QCGA, the algorithm is applied to solve function optimization problems, knapsack problems, and to discover frequent structures from time series. Experimental results show that QCGA has good ability of global optimization, and good ability of diversity reservation, which makes it efficient for complex optimization problems. -
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